Youtube Vision MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GEMINI_API_KEY | Yes | Your Google Gemini API key | |
| GEMINI_MODEL_NAME | No | The specific Gemini model to use (e.g., gemini-1.5-flash) | gemini-2.0-flash |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| summarize_youtube_videoC | Generates a summary of a given YouTube video URL using Gemini Vision API. |
| ask_about_youtube_videoB | Answers a question about the video or provides a general description if no question is asked. |
| extract_key_momentsC | Extracts key moments (timestamps and descriptions) from a given YouTube video. |
| list_supported_modelsB | Lists available Gemini models that support the 'generateContent' method. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
The tools have mostly distinct purposes, but 'ask_about_youtube_video' and 'summarize_youtube_video' could be confused as both provide descriptive outputs about video content. However, 'ask_about_youtube_video' is question-driven while 'summarize_youtube_video' is general, and the other tools ('extract_key_moments', 'list_supported_models') are clearly differentiated.
Three tools follow a consistent verb_noun pattern ('ask_about_youtube_video', 'extract_key_moments', 'summarize_youtube_video'), but 'list_supported_models' deviates by using 'list' instead of a more descriptive verb like 'get' or 'retrieve', and it lacks the 'youtube_video' domain specificity. This mixed convention reduces predictability.
With 4 tools, the count is reasonable for a focused YouTube video analysis server. It covers core functionalities like description, summarization, moment extraction, and model listing, though it could be slightly expanded for more comprehensive coverage (e.g., adding video metadata retrieval).
The server covers key video analysis tasks (description, summarization, moment extraction) and model support listing, but there are notable gaps. For example, it lacks tools for video metadata (e.g., title, duration, uploader), search capabilities, or interaction with YouTube's API beyond vision-based analysis, which limits agent workflows in broader YouTube contexts.